Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 16 through 30 of 33 found.

Cosmomics: A Survey of Nonlinear Complex Network Sciences

Rossetti, Giulio and Remy Cazabet. Community Discovery in Dynamics: A Survey. ACM Computing Surveys. 51/1, 2020. Italian National Research Council and French National Research Centre information scientists provide a broad tutorial to this persistent modular aspect of temporal network studies. See also Identifying Communities in Dynamic Networks Using Information Dynamics by Zejun Sun, et al in Entropy (22/4, 2020).

Complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. As a “user manual,” this work organizes state-of-the-art methodologies based on their rationale, and their specific instantiation. (Abstract)

Siebert, Bram, et al. The Role of Modularity in Self-Organization Dynamics in Biological Networks. arXiv:2003.12311. University of Limerick and University of Bristol theorists including Malbor Asliani post another 2020 example of how much the presence of these complexity features are commonly accepted as a working explanation. But a contradiction remains between this self-assembling natural reality with universal node/link, modular, system viabilities at every and an older “Ptolemaic” paradigm (Brian Greene 2020) seems to be unaware of these revolutionary findings.

Interconnected ensembles of biological entities are some of the most complex systems that modern science has encountered so far. Many biological networks are now known to be constructed in a hierarchical way with two main properties: short average paths that join two distant nodes (neuronal, species, or protein patches) and a high proportion of nodes in modular aggregations. Here we show that network modularity is vital for the formation of self-organising patterns of functional activity. We show that spatial patterns at the modular scale can develop in this case, which may explain how spontaneous order in biological networks follows their modular structures. We test our results on real-world networks to confirm the important role of modularity for macro-scale patterns. (Abstract excerpt)

Freitas, Diogo, et al. Particle Swarm Optimization: A Historical Review Up. Entropy. 22/3, 2020. University of Madeira, Portugal computer engineers survey many ways since the 1990s that this mathematic model of how natural evolutionary systems finesse and optimize iterative solutions has found practical utility. It is currently being joined with and enhanced by artificial neural networks for even more applications. Altogether the review implies and conveys the computational source that guides our life and community.

Exponential growth in data generation and big data science has created an imperative for low-power, high-density information storage. This need has motivated research into multi-level memory devices capable of storing multiple bits per device because their memory state is intrinsically analog. Furthermore, much of the data they will store, along with the subsequent operations, are analog-valued. However the current storage paradigm is quantized for use with digital systems. Here, we recast storage as a communication problem, which allows us to use ideas from analog coding and show that analog-valued emerging memory devices can achieve higher capacities.. (Abstract excerpt)

Zarcone, Ryan, et al. Analog Coding in Emerging Memory Systems. Nature Scientific Reports. 10/6831, 2020. We cite this entry by ten UC Berkeley, Stanford, IBM Research, Macronix International, and Google Brain researchers including Jesse Engel as another example, akin to Miguel Nicolelis’ neuroscience (search), of the optimum value of complementary digital byte and analog link pairings. These archetypal modes are now seen to hold across nature’s genesis, except for political elections where they remain pitted against each other, unaware of any scientific findings.

Exponential growth in data generation and big data science has created an imperative for low-power, high-density information storage. This need has motivated research into multi-level memory devices capable of storing multiple bits per device because their memory state is intrinsically analog. Furthermore, much of the data they will store, along with the subsequent operations, are analog-valued. However the current storage paradigm is quantized for use with digital systems. Here, we recast storage as a communication problem, which allows us to use ideas from analog coding and show that analog-valued emerging memory devices can achieve higher capacities. (Abstract excerpt)

Fan, Jingfang, et al. Universal Gap Scaling in Percolation. Nature Physics. April, 2020. We cite this technical entry by JF, Jun Meng, Yang Liu, Abbas Saberi, and Jurgen Kurths, Potsdam Institute for Climate Impact Research, along with Jan Nagler, Deep Dynamics Group, Frankfurt School of Finance as another current finding about the universal presence, so it seems, from physical networks to everywhere else such as cells, brains, hearts, genomes, and onto to linguistic information. Cosmic to cultural nature can now indeed be found to draw upon and express a single, infinitely recurrent, critical condition.

Universality is a principle that underlies many critical phenomena from epidemic spreading to the emergence of connectivities in networks. Percolation, the transition to global connectedness on gradual addition of links, may exhibit substantial gaps in the size of the largest connected network component. We uncover that the largest gap statistics is governed by extreme-value theory. This allows us to unify continuous and discontinuous percolation by virtue of universal critical scaling functions. This links extreme-value statistics to universality and criticality in percolation. (Abstract)

yang, Ruochen and Paul Bogdan. Controlling the Multifractal Generating Measures of Complex Networks. Nature Scientific Reports. 10/5541, 2020. In this special year, University of Southern California computer scientists (search PB) add to confirmations of a common presence of self-similar forms and functions across nature’s array from geologic to genomic, cerebral and onto our behavioral activities. See also in regard Quantifying Emergence and Self-Organization of Microbial Communities by V. Balaban, et al (USC) in NSR (8/12416, 2018).

Self-repeating patterns and multifractality exist in many real-world complex systems such as brain, genetic, geoscience, and social networks. To better comprehend the multifractal behavior in the real networks, we propose the weighted multifractal graph to model the spatiotemporal complexity and heterogeneity encoded in interaction weights. We apply this approach to two specific complex systems, namely (i) the chromosome interactions of yeast cells in quiescence and in exponential growth, and (ii) the brain networks of healthy people and patients exhibiting mild cognitive impairment leading to Alzheimer disease. We find that our method provides a novel way to understand the self-similar structure of complex networks and to discriminate network structures. (Abstract excerpt)

From a geometrical perspective, many large-scale complex networks from sociology and biology exhibit self-similar and multifractal characteristics. Multifractal geometric analysis makes it possible to capture the heterogeneous and multiscale interaction rules of large networks . It efficiently characterizes large-scale complex systems and can be employed to measure nodes similarity and detect community structures. For instance, the multifractality of geochemistry mapping explains the element concentration values distribution and spatial covariance structure in rock samples. (1)

Sherman, Rachel and Steven Salzberg. Pan-Genomics in the Human Genome Era. Nature Reviews Genetics. 21/243, 2020. Johns Hopkins University computational biologists describe an expansion of the multinational project to sequence all creaturely genomes so as to achieve an entire integrative pan-species genome database. Such an accomplishment just now possible can help preserve biodiversity and converse environments.

Since the early genome era, the scientific community has relied on a single “reference” genome for each species. As sequencing costs dropped, thousands of new genomes have been sequenced which led us to realize that a single reference genome is inadequate. By sampling a diverse set of individuals, one can begin to assemble a pan-genome: a collection of all the DNA sequences that occur in a species. Here we review efforts to create pan-genomes for an array of species from bacteria to humans, and consider computational methods that have been proposed to capture, interpret and compare pan-genome data. (Abstract excerpt)

Systems Evolution: A 21st Century Genesis Synthesis

Phillips, James. Self-Organized Networks: Darwinian Evolution of Dynein Rings, Stalks, and Stalk Heads. Proceedings of the National Academy of Sciences. 117/7799, 2020. In this integral year, a veteran Rutgers University biophysicist describes sees these cellular formations as good examples of how nature organizes and orders itself. Phillips finds this dynamic patterning to be so suitable and robust that its self-making method could appear as a natural “design.” See also Self-assembly, Buckling and Density-invariant Growth of Three-dimensional Vascular Networks by Julius Kirkegaard, et al in the Journal of the Royal Society Interface. (October 2019) for a similar, concurrent view.

Cytoskeletons are self-organized networks based on polymerized proteins: actin, tubulin, and driven by motor proteins, such as myosin, kinesin, and dynein. Their positive Darwinian evolution enables them to approach optimized, universal functionality (self-organized criticality). Dynein binds to tubulin through two coiled coil stalks and a stalk head. The energy used to alter the head binding and propel cargo along tubulin is supplied by ATP. Here, we show how many details of this interaction by water waves can be quantified by thermodynamic scaling. (Abstract excerpt)

Dynein is a family of cytoskeletal motor proteins that move along microtubules in cells. They convert the chemical energy stored in ATP to mechanical work.

Earth Life Emergence: Development of Body, Brain, Selves and Societies

Bartlett, Stuart and Michael Wong. Defining Lyfe in the Universe. Life. 10/4, 2020. CalTech and University of Washington astrobiologists scope out an expansive definition of living systems across a wide cosmic span so as to aid understandings of what they are and how vitality began at all. In regard, “four pillars” of autocatalysis, dissipation, homeostasis and learning are cited along with “three privileged functions” of replication, metabolism, and compartments. These features are seen to resolve the RNA first and other issues while broadening the presence of universal animation.

Life represents life as we know it; it uses the specific disequilibria and classes of components of earthly life. Life is an autocatalytic network of organometallic chemicals in aqueous solution that records and processes information about its environment and achieves dynamical order by dissipating any disequilibria. Lyfe represents any hypothetical phenomenon in the universe that fulfills these processes of the living state, regardless or components that it harnesses or uses. Lyfe maintains a low-entropy state via dissipation and disequilibria conversions, utilizes autocatalytic networks to achieve nonlinear growth and proliferation, employs homeostatic regulation to maintain stability and acquires information about its environment. (6)

Autocatalysis: The ability of a system to exhibit exponential growth of representative measures of size or population in ideal conditions. The property of autocatalysis can appear as self-catalysis, cross-catalysis, and network autocatalysis. Learning: The ability of a system to record information about its external and internal environment, process it, and carry out actions that feed back positively on its probability of surviving/proliferating. (7)

Nalaban, Valeriu, et al. Quantifying Emergence and Self-Organization of Enterobacter cloacae Microbial Communities. Nature Scientific Reports. 8/12416, 2020. We cite this entry by University of Southern California bioengineers as an example of the late 2010s full scale admission of these innate title forces and forms as they serve to distinguish and pervade life’s oriented gestation.

From microbial communities to cancer cells, many complex collectives embody emergent and self-organising behaviour. As a result, cells develop composite features such as formation of aggregates or expression of specific genes due to cell-cell interactions. Currently, we lack a universal mathematics to analyze the collective behaviour of biological swarms. We propose a multifractal inspired framework to measure the degree of emergent self-organisation from scarce spatial data and apply it to evolution of the arrangement of Enterobacter cloacae aggregates. Our method could identify these patterns and dynamics changes within the bacterial population. (Abstract)

Twomey, Colin, et al. Searching for Structure in Collective Systems. Theory in Biosciences. March, 2020. . University of Pennsylvania, Princeton University and Humboldt University social biologists advance the study of ubiquitous creaturely assemblies by way of deep network principles. In regard, a middle scale mutuality between semi-autonomous members and overall clusters is found to best provide the viability that groupings achieve and require.

Collective systems such as fish schools, bird flocks, and neural networks are comprised of many mutually-influencing individuals, often without leaders, hierarchies, or persistent relationships. The remarkably organized group-level behaviors readily observable in these systems contrast with the ad hoc, often vicarious, complex interactions among their constituents. While these individual dynamics factor into group-level coordination, they do not reflect its macroscopic properties. Rather, the source of group cohesion may be better described at some intermediate, mesoscopic scale. We introduce a novel method from information-theoretic principles to find a compressed description of a system based on the actions and mutual dependencies of its constituents, which reveals the natural structure of the collective. (Abstract excerpt)

Valentini, Gabriele, et al. Division of Labour Promotes the Spread of Information in Colony Emigrations by the Ant Temnothorax rugatulus.. Proceedings of the Royal Society B. April, 2020. As this collaborative project to quantify animal groupings goes forth, here eight Arizona State University scientists including Sara Walker and Stephen Pratt point out the importance of steady clear communications for communal coherence and survival success.

The fitness of group-living animals depends on how members share information for decision-making. Theoretical studies have shown that collective choices can emerge in a homogeneous group of individuals who follow identical rules, but real animals are heterogeneous in composition. In social insects, for example, the transmission and processing of information is influenced by a well-organized division of labour. In this paper, we look at nest choices during colony emigrations of the ant Temnothorax rugatulus and the behavioural heterogeneity of workers. Using clustering methods and network analysis, we identify four primary, secondary, passive and wandering castes which covering the spread of information during an emigration. (Abstract excerpt)

Ba, Rui, et al. Analysis of Multifractal and Organization/Order Structure in Suomi-NPP VIIRS Normalized Difference Vegetation Index Series of Wildfire Sites. Entropy. 22/4, 2020. Circa 2004, any perception, let alone proof, of endemic patterns by which to untangle nature were sparse at best. A worldwide decade and half later systems ecologists from China and Italy can describe, along with similar works, the actual presence of mathematic patternings in self-similar scalar array everywhere. One might ask and wonder again how does this deep animate order come to be, whatever agency put it there in the first place?

Fortrat, Jacques-Olivier. Zipf’s Law of Vasovagal Heart Rate Variability Sequences. Entropy. 22/4, 2020. This entry by a UMR CNRS, Centre Hospitalier Universitaire Angers, France systems physiologist notably proceeds to find complexity phenomena at similar effect even in active cardiac function. As the quotes say, not only does its critical poise serve an optimum viability, but by this feature, the vital heart gains an affinity with brains, other organs and widely beyond. These latest findings set aside a homeostatic equilibrium model for a 21st century dynamic self-organization. Further afield, a parallel to linguistic patterns becomes evident, with beats akin to words. In this 2020 a true unity of heart, mind and prose/poetry sensitivity can be appreciated. See also Self-Organization of Blood Pressure Regulation by Fortrat and Claude Gharib in Frontiers in Physiology (March 30, 2016), Physical Mathematic Evaluation of the Cardiac Dynamic Applying the Zipf-Mandelbrot Law by Javier Oswaldo-Rodriguez, et al in Journal of Modern Physics (6/1881, 2015) and Day and Night Changes of Cardiovascular Complexity by Paolo Castiglioni, et al in Entropy (22/4, 2020).

Cardiovascular self-organized criticality (SOC) has recently been demonstrated by studying vasovagal sequences. These sequences combine bradycardia and a decrease in blood pressure. Our primary aim was to verify whether SOC could be studied by solely observing bradycardias and by showing their distribution according to Zipf’s law. Bradycardias are distributed according to Zipf’s law, providing clear insight into cardiovascular SOC. Bradycardia distribution could provide an interesting diagnosis tool for some cardiovascular diseases. (Abstract)

Self-organized criticality has emerged as a major topic in the study of dynamical systems and as a unifying theory across science fields, including physics, chemistry, ecology, and biology. A better understanding of its meaning and implications for the cardiovascular system is needed. Zipf’s law has initially been described based on word occurrence in a text: the frequency of any word in a text is inversely proportional to its rank of occurrence. This law has been inscribed into beat-by-beat recordings of the heart rate. These recordings show a linear distribution of nonspecific consecutive heart rate sequences across several beats, these sequences being the “words” of the cardiovascular system “language”. (2)

In this study, we tried to stay close to the natural physiological language of the cardiovascular system. This approach allowed us to define a simple method with strong evidence of Zipf’s law in the cardiovascular dynamics. However, further studies may help to
better define the natural cardiovascular language to better characterize Zipf’s law and the self-organized properties of the cardiovascular function. (9)

Popiel, Nicholas, et al. The Emergence of Integrated Information, Complexity, and “Consciousness” at Criticality. Entropy. 22/3, 2020. An international neurotheorist collaboration posted at Western University, Canada, Monash University, Australia, and Research in Advanced Neurohabilitation, Italy suggests a way that the “critical brain hypothesis” (Chialvo, et al) can be joined with IIT so to reveal a similar poise in this model. Once again this state of dynamic balance is seen to be natural evolution’s preferred optimum.

A growing body of evidence has emerged suggesting that many disparate natural, and particularly biological, phenomena reside in a critical regime of dynamics on the cusp between order and disorder. More specifically, it has been shown that models tuned to criticality exhibit similar dynamics to the brain, which, has led to the emergence of the Critical Brain Hypothesis. Systems tuned to criticality exhibit a number of useful informational properties that allow for the efficient distribution of, and susceptibility to, information. (1)

Ultimately, this study is best framed in the context of the emerging complexity of our world. The brain is one of the most complex objects ever studied and the theory of it acting critically is gaining credence. New research into critical systems has shown that criticality may be useful for learning, and for optimizing information processing. Phase transitions and criticality are gaining more relevance, and the evidence in this paper demonstrates that by defining consciousness with IIT and using the Ising model as a substrate, ‘consciousness’ undergoes a phase transition at criticality in the investigated neural network motifs. This, when combined with evidence that the brain may be critical, suggests that ‘consciousness’ may simply arise out of the tendency of the brain to self-organize towards criticality. (8-9)

Previous   1 | 2 | 3  Next